• Journal of radiological science and technology
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• v.24 no.1
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• pp.33-41
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• 2001

This study is to get preliminary data for an effectiveness evaluation of abdominal examination and improvement of it. Abnormal cases of abdominal ultrasonography are classified by sex, frequency, diagnosis and age. 4,924 examinees were included at a university hospital of health promotion center from January to December in 1999. The results are as follows. 1. According to the distribution of sex, there are more male patients(55.0%) than females patients(48.0%). For men, 40's showed the highest percentage among examinees. For women, 50's were the highest. 2. The reason that they visited the health promotion center was that 'they wanted to check their health status'. This answers were reported the highest(59.3%). 3. Patients that had abnormal cases of abdominal ultrasonography were 48.3%. Liver, kidney, gallbladder showed the highest percentage of abnormal cases in order of organs. Additionally, abnormal cases were discovered in liver cases. 4. According to the frequency of abnormal cases among examinees, the slight fatty liver were the highest regardless of sex. Men had the slight fatty liver, kidney simple cyst, liver calcification and liver simple cyst in order of abnormal cases. Women showed the slight fatty liver kidney simple cyst, kidney calcification, liver simple cyst, and blood vessel tumor in order of abnormal cases. 5. For the abnormal cases of liver by sex and age, the 50's reported the highest number of abnormal cases in men(299 patients). In addition, 60's had the highest of disease rata 47.8%. For women, 50's reported the highest number of abnormal cases(361 patients). Over 70's patients had the highest of disease rata 52.6%. For kidney, men and women showed the highest number of abnormal cases -62 vs 44 respectively. Over 70's patients had the highest percentage of disease rata-23.2% vs 14.0% respectively. For gallbladder, the number of abnormal cases were the most in men's 60's (31 patients) and in women's in the same age group (32patients). Disease showed the highest percentage in men's 60's(7.6%) and in women's 70's (14.0%). 6. According to malignant tumor, 17patients were liver cancer, 2patients stomach ca and 1pt kidney cancer. 7. The relationship between the malignant tumor and the examination motive was that 'they wanted to check their health status(41.0%)' and 'regular checkup (24.0%)'.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.905-915
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• 2012

This paper suggests the online method to identify normal and abnormal state of water quality on the ocean USN. To define normal of the ocean water quality, we utilize the negative selection algorithm of artificial immunity system which has self and nonself identification characteristics. To distinguish abnormal status, normal state set of the ocean water quality needs to be defined. For this purpose, we generate normal state set base on mutations of each data and mutation of the data as logical product. This mutated normal (or self) sets used to identify abnormal status of the water quality. We represent the experimental result about mutated self set with the Gaussian function. Through setting the method on the ocean sensor logger, we can monitor whether the ocean water quality is normal or abnormal state by online.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.500-510
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• 2023

Passive sonar signals mainly contain both normal and abnormal signals. The abnormal signals mixed with normal signals are primarily detected using an AutoEncoder (AE) that learns only normal signals. However, existing AEs may perform inaccurate detection by reconstructing distorted normal signals from mixed signal. To address these limitations, we propose an abnormal signal detection model based on a Recurrent Neural Network (RNN) and vector quantization. The proposed model generates a codebook representing the learned latent vectors and detects abnormal signals more accurately through the proposed search process of code vectors. In experiments using publicly available underwater acoustic data, the AE and Variational AutoEncoder (VAE) using the proposed method showed at least a 2.4 % improvement in the detection performance and at least a 9.2 % improvement in the extraction performance for abnormal signals than the existing models.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.151-155
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• 2019

Recently heart attack is 80% of the sudden death of elderly. The causes of a heart attack are complex and sudden, and it is difficult to predict the onset even if prevention or medical examination is performed. Therefore, early diagnosis and proper treatment are the most important. In this paper, we show the accuracy of normal and abnormal classification with neural network using weighted fuzzy function for accurate and rapid diagnosis of myocardial infarction. The data used in the experiment was data from the UCI Machine Learning Repository, which consists of 14 features and 303 sample data. The algorithm for feature selection uses the average of weight method. Two features were selected and removed. Heart attack was classified into normal and abnormal(1-normal, 2-abnormal) using the average of weight method. The test result for the diagnosis of heart attack using a weighted fuzzy neural network showed 87.66% accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.1009-1015
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• 2017

Recently, the wide spread of IoT (Internet of Things) based technology enables the accumulation of big biometric data on livestock. The availability of big data allows the application of diverse machine learning based algorithm in the field of agriculture, which significantly enhances the productivity of farms. In this paper, we propose an abnormal livestock detection algorithm based on deep learning, which is the one of the most prominent machine learning algorithm. In our proposed scheme, the livestock are divided into two clusters which are normal and abnormal (disease) whose biometric data has different characteristics. Then a deep neural network is used to classify these two clusters based on the biometric data. By using our proposed scheme, the normal and abnormal livestock can be identified based on big biometric data, even though the detailed stochastic characteristics of biometric data are unknown, which is beneficial to prevent epidemic such as mouth-and-foot disease.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.63-69
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• 2021

In this study, we established an algorithm to determine whether the work in the gas facility is a normal work or an abnormal work using the threshold of the reconstruction error of the autoencoder. This algorithm do deep learning the autoencoder only with time-series data of a normal work, and derives the optimized threshold of the reconstruction error of the normal work. We applied this algorithm to the time series data of the new work to get the reconstruction error, and then compare it with the reconstruction error threshold of the normal work to determine whether the work is normal work or abnormal work. In order to train and validate this algorithm, we defined the work in a virtual gas facility, and constructed the training data set consisting only of normal work data and the validation data set including both normal work and abnormal work data.